To study the performance of CAC as a function of these 4 parameters, we modify the make classification function of sklearn library [1]}, to generate custom datasets with varying ICS, OCS, and \(K\) . We find that the performance of CAC improves with increasing ICS. This is in line with our hypothesis that if the classes are well separated within the clusters, then it will improve the performance of the classifiers. The performance of CAC does not depend much on the OCS. As the number of natural clusters (K) in the dataset increases, the performance of CAC decreases. On the other hand, the performance is better when \(k>K\) .
